{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Understanding Structured Point Clouds (SPCs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Structured Point Clouds (SPC) is a differentiable, GPU-compatible, spatial-data structure which efficiently organizes 3D geometrically sparse information in a very compressed manner.\n",
    "\n",
    "![alt text](../samples/spcTeapot.png \"Structured Point Cloud Hierarchy\")\n",
    "\n",
    "<b> When should you use it? </b>\n",
    "* The SPC data structure is very general, which makes it <mark>a suitable building block for a variety of applications</mark>.\n",
    "* Examples include: \n",
    "    * [Representation & rendering of implicit 3D surfaces](https://nv-tlabs.github.io/nglod/)\n",
    "    * Convolutions on voxels, meshes and point clouds\n",
    "    * and more..\n",
    "\n",
    "SPCs are easily convertible from point clouds and meshes, and can be optimized to represent encoded neural implicit fields.\n",
    "\n",
    "<b> In this tutorial you will learn to: </b>\n",
    "1. Construct a SPC from triangular meshes and point clouds.\n",
    "2. Visualize the SPC using ray-tracing functionality.\n",
    "3. Become familiar with the internals of kaolin's SPC data structure\n",
    "\n",
    "Practitioners are encouraged to view the [documentation](https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.spc.html?highlight=spc#kaolin-ops-spc) for additional details about the internal workings of this data structure. <br>\n",
    "This tutorial is best run locally to observe the full output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial assumes a minimal version of [kaolin v0.10.0](https://kaolin.readthedocs.io/en/latest/notes/installation.html). <br>\n",
    "In addition, the following libraries are required for this tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install -q matplotlib\n",
    "!pip install -q termcolor\n",
    "!pip install -q ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import ipywidgets as widgets\n",
    "from termcolor import colored\n",
    "\n",
    "import kaolin as kal\n",
    "from spc_formatting import describe_octree, color_by_level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To study the mechanics of the SPC structure, we'll need some auxilary functions (you may skip for now): <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def describe_tensor(torch_tensor, tensor_label, with_shape, with_content):\n",
    "    if with_shape:\n",
    "        print(f'\"{tensor_label}\" is a {torch_tensor.dtype} tensor of size {tuple(torch_tensor.shape)}')\n",
    "    if with_content:\n",
    "        print(f'Raw Content: \\n{torch_tensor.cpu().numpy()}')\n",
    "        \n",
    "\n",
    "def convert_texture_to_torch_sample_format(texture):\n",
    "    \"\"\" Convert to (1, C, Tex-H, Tex-W) format \"\"\"\n",
    "    return texture.unsqueeze(0).type(sampled_uvs.dtype).permute(0, 3, 1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminaries: Load Mesh and sample as Point Cloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughout this tutorial we'll be using a triangular mesh as an example. <br>\n",
    "First, we import the mesh using kaolin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded mesh with 642 vertices, 1280 faces and 1 materials.\n"
     ]
    }
   ],
   "source": [
    "# Path to some .obj file with textures\n",
    "mesh_path = \"../samples/colored_sphere.obj\"\n",
    "mesh = kal.io.obj.import_mesh(mesh_path, with_materials=True)\n",
    "\n",
    "print(f'Loaded mesh with {len(mesh.vertices)} vertices, {len(mesh.faces)} faces and {len(mesh.materials)} materials.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll oversample the mesh faces to make sure our SPC structure is densely populated and avoids \"holes\"\n",
    "at the highest resolution level.\n",
    "\n",
    "Note that our mesh face-vertices are mapped to some texture coordinates.\n",
    "Luckily, kaolin has a `sample_points` function that will take care of interpolating these coordinates for us. \n",
    "The sampled vertices will be returned along with the interpolated uv coordinates as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampled 1000000 points over the mesh surface:\n",
      "sampled_verts is a tensor with batch size 1, with 1000000 points of 3D coordinates.\n",
      "sampled_uvs is a tensor with batch size 1, representing the corresponding 1000000 2D UV coordinates.\n"
     ]
    }
   ],
   "source": [
    "# Sample points over the mesh surface\n",
    "num_samples = 1000000\n",
    "\n",
    "# Load the uv coordinates per face-vertex like \"features\" per face-vertex,\n",
    "# which sample_points will interpolate for new sample points.\n",
    "# mesh.uvs is a tensor of uv coordinates of shape (#num_uvs, 2), which we consider as \"features\" here\n",
    "# mesh.face_uvs_idx is a tensor of shape (#faces, 3), indexing which feature to use per-face-per-vertex\n",
    "# Therefore, face_features will be of shape (#faces, 3, 2)\n",
    "face_features = mesh.uvs[mesh.face_uvs_idx]\n",
    "\n",
    "# Kaolin assumes an exact batch format, we make sure to convert from:\n",
    "# (V, 3) to (1, V, 3)\n",
    "# (F, 3, 2) to (1, F, 3, 2)\n",
    "# where 1 is the batch size\n",
    "batched_vertices = mesh.vertices.unsqueeze(0)\n",
    "batched_face_features = face_features.unsqueeze(0)\n",
    "\n",
    "# sample_points is faster on cuda device\n",
    "batched_vertices = batched_vertices.cuda()\n",
    "faces = mesh.faces.cuda()\n",
    "batched_face_features = batched_face_features.cuda()\n",
    "\n",
    "sampled_verts, _, sampled_uvs = kal.ops.mesh.trianglemesh.sample_points(batched_vertices,\n",
    "                                                                        faces,\n",
    "                                                                        num_samples=num_samples,\n",
    "                                                                        face_features=batched_face_features)\n",
    "\n",
    "print(f'Sampled {sampled_verts.shape[1]} points over the mesh surface:')\n",
    "print(f'sampled_verts is a tensor with batch size {sampled_verts.shape[0]},',\n",
    "      f'with {sampled_verts.shape[1]} points of {sampled_verts.shape[2]}D coordinates.')\n",
    "\n",
    "print(f'sampled_uvs is a tensor with batch size {sampled_uvs.shape[0]},',\n",
    "      f'representing the corresponding {sampled_uvs.shape[1]} {sampled_uvs.shape[2]}D UV coordinates.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To finish our setup, we'll want to use the UV coordinates to perform texture sampling and obtain the RGB color of each point we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vertices is a tensor of torch.Size([1000000, 3])\n",
      "vertex_colors is a tensor of torch.Size([1000000, 3])\n"
     ]
    }
   ],
   "source": [
    "# Convert texture to sample-compatible format\n",
    "diffuse_color = mesh.materials[0]['map_Kd']    # Assumes a shape with a single material\n",
    "texture_maps = convert_texture_to_torch_sample_format(diffuse_color) # (1, C, Th, Tw)\n",
    "texture_maps = texture_maps.cuda()\n",
    "\n",
    "# Sample colors according to uv-coordinates\n",
    "sampled_uvs = kal.render.mesh.utils.texture_mapping(texture_coordinates=sampled_uvs,\n",
    "                                                    texture_maps=texture_maps,\n",
    "                                                    mode='nearest')\n",
    "# Unbatch\n",
    "vertices = sampled_verts.squeeze(0)\n",
    "vertex_colors = sampled_uvs.squeeze(0)\n",
    "\n",
    "# Normalize to [0,1]\n",
    "vertex_colors /= 255\n",
    "\n",
    "print(f'vertices is a tensor of {vertices.shape}')\n",
    "print(f'vertex_colors is a tensor of {vertices.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Create & Visualize SPC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the SPC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by converting our Point Cloud of continuous 3D coordinates to a Structured Point Cloud. <br>\n",
    "\n",
    "`unbatched_pointcloud_to_spc` will return a `Spc` object, a data class holding all Structured Point Cloud related information. <br>\n",
    "At the core of this object, points are kept in quantized coordinates using a compressed octree. <br>\n",
    "\n",
    "The returned object contains multiple low-level data structures which we'll explore in details in the next section.\n",
    "For now keep in mind that its important fields: `octree`, `features`, `point_hierarchy`, `pyramid` and `prefix`, represent our data structure.\n",
    "\n",
    "When constructing a `Spc` object, the resolution of quantized coordinates can be controlled by the octree `level` arg, such that: $resolution=2^{level}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our SPC will contain a hierarchy of multiple levels\n",
    "level = 3\n",
    "spc = kal.ops.conversions.pointcloud.unbatched_pointcloud_to_spc(vertices, level, features=vertex_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set-up the camera"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SPC data structure can be efficiently visualized using ray-tracing ops. <br>\n",
    "\n",
    "Note that SPC also supports differentiable rendering. In this tutorial we'll limit our demonstration to rendering this data structure efficiently. <br>\n",
    "Differentiable ray-tracing is beyond the scope of this guide, and will be covered in future tutorials."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin our ray tracing implementation, we'll first need to set up our camera view and [generate some rays](https://www.scratchapixel.com/lessons/3d-basic-rendering/ray-tracing-generating-camera-rays). <br>\n",
    "We'll assume a pinhole camera model, and use the `look_at` function, which sets up a camera view originating at position `camera_from`, looking towards `camera_to`. <br>\n",
    "`width`, `height`, `mode` and `fov` will determine the dimensions of our view."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _normalized_grid(width, height, device='cuda'):\n",
    "    \"\"\"Returns grid[x,y] -> coordinates for a normalized window.\n",
    "    \n",
    "    Args:\n",
    "        width, height (int): grid resolution\n",
    "    \"\"\"\n",
    "\n",
    "    # These are normalized coordinates\n",
    "    # i.e. equivalent to 2.0 * (fragCoord / iResolution.xy) - 1.0\n",
    "    window_x = torch.linspace(-1, 1, steps=width, device=device) * (width / height)\n",
    "    window_y = torch.linspace(1,- 1, steps=height, device=device)\n",
    "\n",
    "    coord = torch.stack(torch.meshgrid(window_x, window_y)).permute(1,2,0)\n",
    "    return coord\n",
    "\n",
    "\n",
    "def look_at(camera_from, camera_to, width, height, mode='persp', fov=90.0, device='cuda'):\n",
    "    \"\"\"Vectorized look-at function, returns an array of ray origins and directions\n",
    "    URL: https://www.scratchapixel.com/lessons/mathematics-physics-for-computer-graphics/lookat-function\n",
    "    \"\"\"\n",
    "\n",
    "    camera_origin = torch.FloatTensor(camera_from).to(device)\n",
    "    camera_view = F.normalize(torch.FloatTensor(camera_to).to(device) - camera_origin, dim=0)\n",
    "    camera_right = F.normalize(torch.cross(camera_view, torch.FloatTensor([0,1,0]).to(device)), dim=0)\n",
    "    camera_up = F.normalize(torch.cross(camera_right, camera_view), dim=0)\n",
    "\n",
    "    coord = _normalized_grid(width, height, device=device)\n",
    "    ray_origin = camera_right * coord[...,0,np.newaxis] * np.tan(np.radians(fov/2)) + \\\n",
    "                 camera_up * coord[...,1,np.newaxis] * np.tan(np.radians(fov/2)) + \\\n",
    "                 camera_origin + camera_view\n",
    "    ray_origin = ray_origin.reshape(-1, 3)\n",
    "    ray_offset = camera_view.unsqueeze(0).repeat(ray_origin.shape[0], 1)\n",
    "    \n",
    "    if mode == 'ortho': # Orthographic camera\n",
    "        ray_dir = F.normalize(ray_offset, dim=-1)\n",
    "    elif mode == 'persp': # Perspective camera\n",
    "        ray_dir = F.normalize(ray_origin - camera_origin, dim=-1)\n",
    "        ray_origin = camera_origin.repeat(ray_dir.shape[0], 1)\n",
    "    else:\n",
    "        raise ValueError('Invalid camera mode!')\n",
    "\n",
    "\n",
    "    return ray_origin, ray_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now generate some rays using the functions we've just created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 1048576 rays generated.\n"
     ]
    }
   ],
   "source": [
    "# ray_o and ray_d ~ torch.Tensor (width x height, 3)\n",
    "# represent rays origin and direction vectors\n",
    "ray_o, ray_d = look_at(camera_from=[-2.5,2.5,-2.5],\n",
    "                       camera_to=[0,0,0],\n",
    "                       width=1024,\n",
    "                       height=1024,\n",
    "                       mode='persp',\n",
    "                       fov=30,\n",
    "                       device='cuda')\n",
    "\n",
    "print(f'Total of {ray_o.shape[0]} rays generated.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Render"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're now ready to perform the actual ray tracing. <br>\n",
    "kaolin will \"shoot\" the rays for us, and perform an efficient intersection test between each ray and cell within the SPC structure. <br>\n",
    "In kaolin terminology, <b>nuggets</b> are \"ray-cell intersections\" (or rather \"ray-point\" intersections).\n",
    "\n",
    "<b>nuggets </b> are of represented by a structure of two tensors: `nugs_ridx` and `nugs_pidx`, <br>which form together pairs of `(index_to_ray, index_to_points)`. <br>\n",
    "Both tensors are 1-dimensional tensors of shape (`#num_intersection`,)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 1195190 nuggets were traced.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "octree, features = spc.octrees, spc.features\n",
    "point_hierarchy, pyramid, prefix = spc.point_hierarchies, spc.pyramids[0], spc.exsum\n",
    "nugs_ridx, nugs_pidx, depth = kal.render.spc.unbatched_raytrace(octree, point_hierarchy, pyramid, prefix, ray_o, ray_d, level)\n",
    "print(f'Total of {nugs_ridx.shape[0]} nuggets were traced.\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we're assuming here our surface is opaque, for each ray, we only care about the <b>nugget</b>\n",
    "closest to the camera. <br>\n",
    "Note that per \"ray-pack\", the returned <b>nuggets</b> are already sorted by depth. <br>\n",
    "The method below returns a boolean mask which specifies which <b>nuggets</b> represent a \"first-hit\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "masked_nugs = kal.render.spc.mark_pack_boundaries(nugs_ridx)\n",
    "nugs_ridx = nugs_ridx[masked_nugs]\n",
    "nugs_pidx = nugs_pidx[masked_nugs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, for each ray that hit the surface, a corresponding \"first-hit\" nugget exists. <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. We initialize an empty canvas.\n",
    "image = torch.ones_like(ray_o)\n",
    "\n",
    "# 2. We'll query all first-hit nuggets to obtain their corresponding point-id (which cell they hit in the SPC).\n",
    "ridx = nugs_ridx.long()\n",
    "pidx = nugs_pidx.long() - pyramid[1,level]\n",
    "\n",
    "# 3. We'll query the features auxilary structure to obtain the color.\n",
    "# 4. We set each ray value as the corresponding nugget color.\n",
    "image[ridx] = features[pidx]\n",
    "\n",
    "image = image.reshape(1024, 1024, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Putting it all together, we write our complete `render()` function and display the trace using matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def render(level):\n",
    "    \"\"\" Create & render an image \"\"\"\n",
    "    spc = kal.ops.conversions.pointcloud.unbatched_pointcloud_to_spc(vertices, level, vertex_colors)\n",
    "    octree, features, point_hierarchy, pyramid, prefix = spc.octrees, spc.features, spc.point_hierarchies, spc.pyramids[0], spc.exsum\n",
    "    \n",
    "    nugs_ridx, nugs_pidx, depth = kal.render.spc.unbatched_raytrace(octree, point_hierarchy, pyramid, prefix, ray_o, ray_d, level)\n",
    "    masked_nugs = kal.render.spc.mark_pack_boundaries(nugs_ridx)\n",
    "    nugs_ridx = nugs_ridx[masked_nugs]\n",
    "    nugs_pidx = nugs_pidx[masked_nugs]\n",
    "    \n",
    "    ridx = nugs_ridx.long()\n",
    "    pidx = nugs_pidx.long() - pyramid[1,level]\n",
    "    image = torch.ones_like(ray_o)\n",
    "    image[ridx] = features[pidx]\n",
    "    image = image.reshape(1024, 1024, 3)\n",
    "    return image\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "# Render left image of level 3 SPC\n",
    "image1 = render(level=3)\n",
    "image1 = image1.cpu().numpy().transpose(1,0,2)\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "ax.set_title(\"level 3\", fontsize=26)\n",
    "ax.axis('off')\n",
    "plt.imshow(image1)\n",
    "\n",
    "# Render right image of level 5 SPC\n",
    "image2 = render(level=8)\n",
    "image2 = image2.cpu().numpy().transpose(1,0,2)\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "ax.set_title(\"level 5\", fontsize=26)\n",
    "ax.axis('off')\n",
    "plt.imshow(image2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, putting it all together, we may also construct the following interactive demo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7405b1041b6d4a2a8a060fe0fc6e77e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(IntSlider(value=7, description='<h5>SPC Level:</h5>', layout=Layout(height='100%…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def update_demo(widget_spc_level):\n",
    "    image = render(widget_spc_level)\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.axis('off')\n",
    "    plt.imshow(image.cpu().numpy().transpose(1,0,2))\n",
    "    plt.show()\n",
    "\n",
    "def show_interactive_demo(max_level=10):\n",
    "    start_value = min(7, max_level)\n",
    "    widget_spc_level = widgets.IntSlider(value=start_value, min=1, max=max_level, step=1, orientation='vertical',\n",
    "                                     description='<h5>SPC Level:</h5>', disabled=False,\n",
    "                                     layout=widgets.Layout(height='100%',))\n",
    "    \n",
    "    out = widgets.interactive_output(update_demo, {'widget_spc_level': widget_spc_level})\n",
    "    display(widgets.HBox([widgets.VBox([widget_spc_level]), out]))\n",
    "\n",
    "show_interactive_demo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. SPC internals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we'll explore the various components that make up the [SPC](https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.spc.html?highlight=spc#structured-point-clouds) we've just created. <br>\n",
    "We'll learn how data is stored, and how to view stored data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boilerplate code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's rebuild our SPC object with fewer levels, that will make the internals easier to study. <br>\n",
    "You may customize the number of levels and compare how the output changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "level = 3\n",
    "spc = kal.ops.conversions.pointcloud.unbatched_pointcloud_to_spc(vertices, level, features=vertex_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, let's see what we've got."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### octree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first field we'll look into, `octrees`, keeps the entire geometric structure in a compressed manner. <br>\n",
    "This is a huge advantage, as this structure is now small enough to fit our sparse data, which makes it very efficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "octree = spc.octrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"octree\" is a torch.uint8 tensor of size (17,)\n",
      "Raw Content: \n",
      "[255 128  64  32  16   8   4   2   1 127 191 223 239 247 251 253 254]\n",
      "\n",
      "\"octrees\" represents a hierarchy of 17 octree nodes.\n",
      "Let's have a look at the binary representation and what it means:\n",
      "\n",
      "Level \u001B[31m#1, \u001B[0mPath\u001B[31m*\u001B[0m,        11111111\n",
      "Level \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m,      10000000\n",
      "Level \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m,      01000000\n",
      "Level \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m,      00100000\n",
      "Level \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m,      00010000\n",
      "Level \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m,      00001000\n",
      "Level \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m,      00000100\n",
      "Level \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m,      00000010\n",
      "Level \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m,      00000001\n",
      "Level \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m-\u001B[32m8\u001B[0m,    01111111\n",
      "Level \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m-\u001B[32m7\u001B[0m,    10111111\n",
      "Level \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m-\u001B[32m6\u001B[0m,    11011111\n",
      "Level \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m-\u001B[32m5\u001B[0m,    11101111\n",
      "Level \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m-\u001B[32m4\u001B[0m,    11110111\n",
      "Level \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m-\u001B[32m3\u001B[0m,    11111011\n",
      "Level \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m-\u001B[32m2\u001B[0m,    11111101\n",
      "Level \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m-\u001B[32m1\u001B[0m,    11111110\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d85782377fb84deb92b2691b6c81c43d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Output(layout=Layout(border='0.2px dashed black')),))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "describe_tensor(torch_tensor=octree, tensor_label='octree', with_shape=True, with_content=True)\n",
    "\n",
    "print(f'\\n\"octrees\" represents a hierarchy of {len(octree)} octree nodes.')\n",
    "print(f\"Let's have a look at the binary representation and what it means:\\n\")\n",
    "\n",
    "describe_octree(octree, level)\n",
    "\n",
    "text_out = widgets.Output(layout={'border': '0.2px dashed black'})\n",
    "with text_out:\n",
    "    print('How to read the content of octrees?')\n",
    "    print('- Each entry represents a single octree of 8 cells --> 8 bits.')\n",
    "    print('- The bit position determines the cell index, in Morton Order.')\n",
    "    print('- The bit value determines if the cell is occupied or not.')\n",
    "    print(f'- If a cell is occupied, an additional octree may be generated in the next level, up till level {level}.')\n",
    "    print('For example, an entry of 00000001 is a single level octree, where only the bottom-left most cell is occupied.')\n",
    "    \n",
    "display(widgets.HBox([text_out]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Order of octants within partitioned cells\n",
    "![alt text](../samples/octants.png \"Octants\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the field is named in plural.\n",
    "That's because kaolin can batch multiple instances of octrees together within the same object. <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print(spc.batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pay attention that `octrees` uses [packed representation](https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.batch.html?highlight=packed#packed), meaning, there is no explicit batch dimension. <br>\n",
    "Instead, we track the length of each octree instance in a separate field:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"lengths\" is a torch.int32 tensor of size (1,)\n",
      "Raw Content: \n",
      "[17]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6fae162e93024c3fa693944ca1541898",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Output(layout=Layout(border='0.2px dashed black')),))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "octrees_lengths = spc.lengths\n",
    "describe_tensor(torch_tensor=octrees_lengths, tensor_label='lengths', with_shape=True, with_content=True)\n",
    "\n",
    "text_out = widgets.Output(layout={'border': '0.2px dashed black'})\n",
    "with text_out:\n",
    "    print('How to read the content of lengths?')\n",
    "    print(f'- This Spc stores a batch of {len(spc.lengths)} octrees.')\n",
    "    print(f'- The first octree is represented by {spc.lengths[0]} non-leaf cells.')\n",
    "    print(f'- Therefore the information of the first octree is kept in bytes 0-{spc.lengths[0]-1} of the octrees field.')\n",
    "    \n",
    "display(widgets.HBox([text_out]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Advanced users who prefer a non object-oriented lower-level api can also use the following functionality which `kal.ops.conversions.pointcloud.unbatched_pointcloud_to_spc` employs under the hood:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"octrees\" is a torch.uint8 tensor of size (26,)\n",
      "Raw Content: \n",
      "[255 128  64  32  16   8   4   2   1 255 128  64  32  16   8   4   2   1\n",
      " 127 191 223 239 247 251 253 254]\n",
      "\n",
      "\"lengths\" is a torch.int32 tensor of size (2,)\n",
      "Raw Content: \n",
      "[ 9 17]\n"
     ]
    }
   ],
   "source": [
    "from kaolin.ops.spc.points import quantize_points, points_to_morton, morton_to_points, unbatched_points_to_octree\n",
    "\n",
    "# Construct a batch of 2 octrees. For brevity, we'll use the same ocuupancy data for both.\n",
    "# 1. Convert continous to quantized coordinates\n",
    "# 2. Build the octrees\n",
    "\n",
    "points1 = quantize_points(vertices.contiguous(), level=2)\n",
    "octree1 = unbatched_points_to_octree(points1, level=2)\n",
    "\n",
    "points2 = quantize_points(vertices.contiguous(), level=3)\n",
    "octree2 = unbatched_points_to_octree(points2, level=3)\n",
    "\n",
    "# Batch 2 octrees together. For packed representations, this is just concatenation.\n",
    "octrees = torch.cat((octree1, octree2), dim=0)\n",
    "lengths = torch.tensor([len(octree1), len(octree2)], dtype=torch.int32)\n",
    "\n",
    "describe_tensor(torch_tensor=octrees, tensor_label='octrees', with_shape=True, with_content=True)\n",
    "print('')\n",
    "describe_tensor(torch_tensor=lengths, tensor_label='lengths', with_shape=True, with_content=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These structures form the bare minimum required to shift back to high-level api and construct a Spc object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<kaolin.rep.spc.Spc at 0x7f5eed7932e0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kal.rep.spc.Spc(octrees, lengths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far we've lookied into how Structured Point Clouds keep track of occupancy. <br>\n",
    "Next we'll study how they keep track of features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `features` field contains features information per cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = spc.features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In this tutorial, cell features are RGB colors:\n",
      "\"features\" is a torch.float32 tensor of size (56, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c615beaebc6c491cbfcd423113182258",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Output(layout=Layout(border='0.2px dashed black')),))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def paint_features(features):\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.axis('off')\n",
    "    plt.imshow(features.cpu().numpy()[None])\n",
    "    plt.show()\n",
    "    \n",
    "print('In this tutorial, cell features are RGB colors:')\n",
    "describe_tensor(torch_tensor=features, tensor_label='features', with_shape=True, with_content=False)\n",
    "paint_features(features)\n",
    "\n",
    "text_out = widgets.Output(layout={'border': '0.2px dashed black'})\n",
    "with text_out:\n",
    "    print('How to read the content of features?')\n",
    "    print(f'- We keep features only for leaf cells, a total of {features.shape[0]}.')\n",
    "    print(f'- The number of leaf cells can be obtained by summarizing the \"1\" bits at level {spc.max_level},\\n' \\\n",
    "    '  the last level of the octree.')\n",
    "    print(f'- The dimensionality of each attribute is {features.shape[1]} (e.g: RGB channels)')\n",
    "    print('\\nReminder - the highest level of occupancy octree is:')\n",
    "    describe_octree(spc.octrees, level, limit_levels=[spc.max_level])\n",
    "    \n",
    "\n",
    "display(widgets.HBox([text_out]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pyramid & exsum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the occupancy information is [compressed](https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.spc.html?highlight=spc#octree) and [packed](https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.batch.html?highlight=packed#packed), accessing level-specific information consistently involves\n",
    "cumulative summarization of the number of \"1\" bits. <br>\n",
    "It makes sense to calculate this information once and then cache it. <br>\n",
    "The `pyramid` field does exactly that: it keeps summarizes the number of occupied cells per level, and their cumsum, for fast level-indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A pyramid is kept per octree in the batch.\n",
    "# We'll study the pyramid of the first and only entry in the batch.\n",
    "pyramid = spc.pyramids[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"pyramid\" is a torch.int32 tensor of size (2, 5)\n",
      "Raw Content: \n",
      "[[ 1  8  8 56  0]\n",
      " [ 0  1  9 17 73]]\n",
      "\n",
      "How to read the content of pyramids?\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d13bba6a367b413e824c835ca3080a21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Output(layout=Layout(border='0.2px dashed black')), Output(layout=Layout(border='0.2px dashed b…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "describe_tensor(torch_tensor=pyramid, tensor_label='pyramid', with_shape=True, with_content=True)\n",
    "\n",
    "out_left = widgets.Output(layout={'border': '0.2px dashed black'})\n",
    "out_right = widgets.Output(layout={'border': '0.2px dashed black'})\n",
    "\n",
    "print('\\nHow to read the content of pyramids?')\n",
    "\n",
    "with out_left:\n",
    "    print('\"pyramid\" summarizes the number of occupied \\ncells per level, and their cumulative sum:\\n')\n",
    "    for i in range(pyramid.shape[-1]):\n",
    "        if i ==0:\n",
    "            print(f'Root node (implicitly defined):')\n",
    "        elif i+1 < pyramid.shape[-1]:\n",
    "            print(f'Level #{i}:')\n",
    "        else:\n",
    "            print(f'Final entry for total cumsum:')\n",
    "        print(f'\\thas {pyramid[0,i]} occupied cells')\n",
    "        print(f'\\tstart idx (cumsum excluding current level): {pyramid[1,i]}')\n",
    "        \n",
    "with out_right:\n",
    "    print(f'\"octrees\" represents a hierarchy of {len(spc.octrees)} octree nodes.')\n",
    "    print(f\"Each bit represents a cell occupancy:\\n\")\n",
    "    describe_octree(octree, level)\n",
    "    \n",
    "    \n",
    "display(widgets.HBox([out_left, out_right]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, kaolin keeps a complementary field, `exsum`, which tracks the cumulative summarization of bits per-octree to fast access parent-child information between levels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "exsum = spc.exsum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"exsum\" is a torch.int32 tensor of size (18,)\n",
      "Raw Content: \n",
      "[ 0  8  9 10 11 12 13 14 15 16 23 30 37 44 51 58 65 72]\n",
      "\n",
      "How to read the content of exsum?\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14a439fe964b419d8544f87460eeeea3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Output(layout=Layout(border='0.2px dashed black')), Output(layout=Layout(border='0.2px dashed b…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "describe_tensor(torch_tensor=exsum, tensor_label='exsum', with_shape=True, with_content=True)\n",
    "\n",
    "out_left = widgets.Output(layout={'border': '0.2px dashed black'})\n",
    "out_right = widgets.Output(layout={'border': '0.2px dashed black'})\n",
    "\n",
    "print('\\nHow to read the content of exsum?')\n",
    "\n",
    "with out_left:\n",
    "    print('\"exsum\" summarizes the cumulative number of occupied \\ncells per octree, e.g: exclusive sum of \"1\" bits:\\n')\n",
    "    for i in range(exsum.shape[-1]):\n",
    "        print(f'Cells in Octree #{i} start from cell idx: {exsum[i]}')\n",
    "        \n",
    "with out_right:\n",
    "    print(f'\"octrees\" represents a hierarchy of {len(octree)} octree nodes.')\n",
    "    print(f\"Each bit represents a cell occupancy:\\n\")\n",
    "    describe_octree(octree, level)\n",
    "    \n",
    "    \n",
    "display(widgets.HBox([out_left, out_right]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using Spc objects, pyramids are implicitly created the first time they are needed so you don't have to worry about them. <br>\n",
    "For advanced users, the low-level api allows their explicit creation through `scan_octrees`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_level:\n",
      "3\n",
      "\n",
      "pyramid:\n",
      "tensor([[[ 1,  8,  8, 56,  0],\n",
      "         [ 0,  1,  9, 17, 73]]], dtype=torch.int32)\n",
      "\n",
      "exsum:\n",
      "tensor([ 0,  8,  9, 10, 11, 12, 13, 14, 15, 16, 23, 30, 37, 44, 51, 58, 65, 72,\n",
      "        72, 72, 72, 72, 72, 72, 72, 72, 72], device='cuda:0',\n",
      "       dtype=torch.int32)\n"
     ]
    }
   ],
   "source": [
    "lengths = torch.tensor([len(octrees)], dtype=torch.int32)\n",
    "max_level, pyramid, exsum = kal.ops.spc.spc.scan_octrees(octree, lengths)\n",
    "\n",
    "print('max_level:')\n",
    "print(max_level)\n",
    "\n",
    "print('\\npyramid:')\n",
    "print(pyramid)\n",
    "\n",
    "print('\\nexsum:')\n",
    "print(exsum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### point_hierarchies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`point_hierarchies` is an auxilary field, which holds the *sparse* coordinates of each point / occupied cell within the octree, for easier access.\n",
    "\n",
    "Sparse coordinates are packed for all cells on all levels combined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"point_hierarchies\" is a torch.int16 tensor of size (73, 3)\n"
     ]
    }
   ],
   "source": [
    "describe_tensor(torch_tensor=spc.point_hierarchies, tensor_label='point_hierarchies', with_shape=True, with_content=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the information stored in the pyramids field to color the coordinates by level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d7c28d875ca46e697637efc1a43af04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Output(layout=Layout(border='0.2px dashed black')), Output(layout=Layout(border='0.2px dashed b…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_left = widgets.Output(layout={'border': '0.2px dashed black'})\n",
    "out_right = widgets.Output(layout={'border': '0.2px dashed black', 'width': '60%'})\n",
    "\n",
    "max_points_to_display = 17  # To avoid clutter\n",
    "\n",
    "with out_left:\n",
    "    level_idx =0\n",
    "    point_idx = 0\n",
    "    remaining_cells_per_level = spc.pyramids[0,0].cpu().numpy().tolist()\n",
    "\n",
    "    for coord in spc.point_hierarchies:\n",
    "        remaining_cells_per_level[level_idx] -= 1\n",
    "\n",
    "        if point_idx == max_points_to_display:\n",
    "            print(colored(f'skipping more..', level_color))\n",
    "        elif point_idx < max_points_to_display:\n",
    "            level_color = color_by_level(level_idx - 1)\n",
    "            print(colored(f'Level #{level_idx}, Point #{point_idx}, ' \\\n",
    "                          f'Coords: {coord.cpu().numpy().tolist()}', level_color))\n",
    "\n",
    "        if not remaining_cells_per_level[level_idx]:\n",
    "            level_idx += 1\n",
    "            point_idx = 0\n",
    "        else:\n",
    "            point_idx += 1\n",
    "\n",
    "with out_right:\n",
    "    print('How to read the content of point_hierarchies?')\n",
    "    print(f'- Each cell / point is represented by {spc.point_hierarchies.shape[-1]} indices (xyz).')\n",
    "    print('- Sparse coordinates are absolute: \\n  they are defined relative to the octree origin.')    \n",
    "    print('- Compare the point coordinates with the demo below.\\n\\n  Remember: unoccupied cells are not displayed!')\n",
    "    show_interactive_demo(max_level=spc.max_level)\n",
    "\n",
    "display(widgets.HBox([out_left, out_right]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Where to go from here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Structured Point Clouds support other useful operators which were not covered by this tutorial:\n",
    "\n",
    "1. [Convolutions](https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.spc.html?highlight=SPC#kaolin.ops.spc.Conv3d)\n",
    "2. [Querying points by location](https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.spc.html?highlight=SPC#kaolin.ops.spc.unbatched_query)\n",
    "3. [Differential ray-tracing ops](https://kaolin.readthedocs.io/en/latest/modules/kaolin.render.spc.html#kaolin-render-spc)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:kaolin] *",
   "language": "python",
   "name": "conda-env-kaolin-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "00e5ff2bf5e44c8c9b4311e14cee9201": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "021f5c5bb0cb4fc1b71345e047c1332e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "022ca2f0631d4e0099f80b540f6764b6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_7bd49d6189154d62afc3984e530d25ec"
       ],
       "layout": "IPY_MODEL_59163a4df3454e458ad3061ec1ff2358"
      }
     },
     "038214d9bffa4a4dac2eb1d3126ab620": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_4a10fa3318714fd99e7235536766c63e",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of octrees?\n- Each entry represents a single octree of 8 cells --> 8 bits.\n- The bit position determines the cell index, in Morton Order.\n- The bit value determines if the cell is occupied or not.\n- If a cell is occupied, an additional octree may be generated in the next level, up till level 3.\nFor example, an entry of 00000001 is a single level octree, where only the bottom-left most cell is occupied.\n"
        }
       ]
      }
     },
     "06019e51fc4a492c80f21eb24626de00": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "06460c2c1d6741eab0636684e16022bc": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "0696668f859746d68fa2fa7200423ab9": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "0715b93aff5749f89d51804e8a0d7482": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "<h5>SPC Level:</h5>",
       "layout": "IPY_MODEL_bb647370ace741e68ed37756cc10b4df",
       "max": 10,
       "min": 1,
       "orientation": "vertical",
       "style": "IPY_MODEL_25103d90cc60454a985817f59eb13f0c",
       "value": 7
      }
     },
     "07ce6ab72e814e2f885d200054d8c2b0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "08108a391b524809befc36d2938626d9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_022ca2f0631d4e0099f80b540f6764b6",
        "IPY_MODEL_3b82a06bdf3a4cb29c3843536a9b1920"
       ],
       "layout": "IPY_MODEL_80a6d52eb8594fe4a4c68f43ad0e89c0"
      }
     },
     "09275cc785ae4305b2e4f800b98bd6f3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "0a2eec38d87d4f22962c9fc928b9670b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "<h5>SPC Level:</h5>",
       "layout": "IPY_MODEL_405baecfd7a34a088b8461558dc39321",
       "max": 3,
       "min": 1,
       "orientation": "vertical",
       "style": "IPY_MODEL_8f96bac864e34ceca79d4fcc9c4148ac",
       "value": 3
      }
     },
     "0dba0870896341a3b4a8b92f7b49db17": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_ba433165e9ec4dbc96fb694414456010"
       ],
       "layout": "IPY_MODEL_c6e8f7597031435ea093e3c5966f274b"
      }
     },
     "0eb89324c3944e1db2baf3171df1773f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_be36847bf97f490c91a204c6c1fa66bb"
       ],
       "layout": "IPY_MODEL_38e4a192d4a14f17bf5755396e07be04"
      }
     },
     "106014b2958a43f7986981054210a3f8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "1261aedad7f44d9594d480f57ef1622e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "12cd89cd3a7142d99ce99bc4ce8421e5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "13d28d7ed6104281abec8943427dbcc6": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_33c0a02eb55c41398d77ba10d347930c",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"octrees\" represents a hierarchy of 17 octree nodes.\nEach bit represents a cell occupancy:\n\nLevel \u001B[31m#1, \u001B[0mPath\u001B[31m*\u001B[0m,        11111111\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m,      10000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m,      01000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m,      00100000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m,      00010000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m,      00001000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m,      00000100\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m,      00000010\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m,      00000001\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m-\u001B[32m8\u001B[0m,    01111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m-\u001B[32m7\u001B[0m,    10111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m-\u001B[32m6\u001B[0m,    11011111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m-\u001B[32m5\u001B[0m,    11101111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m-\u001B[32m4\u001B[0m,    11110111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m-\u001B[32m3\u001B[0m,    11111011\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m-\u001B[32m2\u001B[0m,    11111101\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m-\u001B[32m1\u001B[0m,    11111110\n"
        }
       ]
      }
     },
     "16b1860e60a444138fc2d643dae567d0": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_b0ba3b72b8764ea380f2e9f9e1b168d4",
        "IPY_MODEL_a7f2e4c4a30b4181b26d9eb2c7e32902"
       ],
       "layout": "IPY_MODEL_12cd89cd3a7142d99ce99bc4ce8421e5"
      }
     },
     "173afffaeaf64066b6eb773580eee392": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "17920dc032a04fb9968a89d20e84f29f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_021f5c5bb0cb4fc1b71345e047c1332e",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of octrees?\n- Each entry represents a single octree of 8 cells --> 8 bits.\n- The bit position determines the cell index, in Morton Order.\n- The bit value determines if the cell is occupied or not.\n- If a cell is occupied, an additional octree may be generated in the next level, up till level 3.\nFor example, an entry of 00000001 is a single level octree, where only the bottom-left most cell is occupied.\n"
        }
       ]
      }
     },
     "17acfce4e50c4221b48e7f404c185b61": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_b9f68c2591da4595ac0f9ede6aba0947",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"octrees\" represents a hierarchy of 17 octree nodes.\nEach bit represents a cell occupancy:\n\nLevel \u001B[31m#1, \u001B[0mPath\u001B[31m*\u001B[0m,        11111111\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m,      10000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m,      01000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m,      00100000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m,      00010000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m,      00001000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m,      00000100\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m,      00000010\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m,      00000001\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m-\u001B[32m8\u001B[0m,    01111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m-\u001B[32m7\u001B[0m,    10111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m-\u001B[32m6\u001B[0m,    11011111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m-\u001B[32m5\u001B[0m,    11101111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m-\u001B[32m4\u001B[0m,    11110111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m-\u001B[32m3\u001B[0m,    11111011\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m-\u001B[32m2\u001B[0m,    11111101\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m-\u001B[32m1\u001B[0m,    11111110\n"
        }
       ]
      }
     },
     "1806f9178c224e9892e73a3e45eee48c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "184d1d6f8e7a4c948ad28730322e22bc": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_c06f4c8abfda4a548e2c254b62814a71",
        "IPY_MODEL_abfbd5d5687443708d121c9841af086b"
       ],
       "layout": "IPY_MODEL_8f9f30c9b96d4a308f74dd3127cf9f8f"
      }
     },
     "185c5cb6233949dc8804860622bd17e6": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "18d711732b56461098f0c68e3b6361c9": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "194d2d330cbe463ea0d9ec6feed97fe7": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_3186a6ac3426472db5f6c3b57490a3a3",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of lengths?\n- This Spc stores a batch of 1 octrees.\n- The first octree is represented by 17 non-leaf cells.\n- Therefore the information of the first octree is kept in bytes 0-16 of the octrees field.\n"
        }
       ]
      }
     },
     "1964d083343845339b0aef35414346ad": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_194d2d330cbe463ea0d9ec6feed97fe7"
       ],
       "layout": "IPY_MODEL_00e5ff2bf5e44c8c9b4311e14cee9201"
      }
     },
     "1b0d2b69f50949d6ba0931eaae9ea654": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "1cd477da1f814e5c83ba80f62d614385": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_038214d9bffa4a4dac2eb1d3126ab620"
       ],
       "layout": "IPY_MODEL_bdddb4b67c1d40fdbc75efc4dc4f240c"
      }
     },
     "1e36d98825aa48cebdb7e67adcb836b2": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "1f4dde12efce436e89585750c8d07e78": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "1fab918b08a842f5a3752406485bf378": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "20d27e9c5b564a34bc82fdb7922b6725": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_513a337a35e343e08b9d24323e015dad"
       ],
       "layout": "IPY_MODEL_18d711732b56461098f0c68e3b6361c9"
      }
     },
     "22dde222a27b42a0875b5395d3ef98f3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "2480e7f508cc421189a344986f3f8334": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "24e36745221b4d9cb213262eb7019f9b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black",
       "width": "60%"
      }
     },
     "25103d90cc60454a985817f59eb13f0c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "25f2ac5125874b719458a773e3d1a301": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_0715b93aff5749f89d51804e8a0d7482"
       ],
       "layout": "IPY_MODEL_b26f6ad199214573998cac894f17a404"
      }
     },
     "29203be51e1b4e2fbd40acaa6b6a21e3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "height": "100%"
      }
     },
     "2933f412929a44cfafd0dbeec0857e32": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_80ba4558ee1f40bcb891d1e905646ac8"
       ],
       "layout": "IPY_MODEL_06019e51fc4a492c80f21eb24626de00"
      }
     },
     "2b4b9f95c4a6426ea60e5080d2b07b31": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "height": "100%"
      }
     },
     "2e5d3befe1fc4429b7acbffe8386ccd1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "307778a007b24c86a6129d4065fef21c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_c2f06a77b15c4912a79917714ae84cd7",
        "IPY_MODEL_4f811b0fa5f046a195d92e20aef0336f"
       ],
       "layout": "IPY_MODEL_3d8801af180740bda3b50944016d9048"
      }
     },
     "3186a6ac3426472db5f6c3b57490a3a3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "335a2a7e5ead4c6d88f004629ce1752f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "33c0a02eb55c41398d77ba10d347930c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "347affd11c4f44e1b2f3823dcb3a28b5": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_4b09b597b0bc48bca1907f3291efb313",
        "IPY_MODEL_4a1ce300101b433d8988be9785142afa"
       ],
       "layout": "IPY_MODEL_4f53ddd23dd24cd6a25c1d88d68ea137"
      }
     },
     "349677fe5b39486b9a83526058aec8cf": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "37a2f184b50e4bb9869dacc1c30cb876": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "37e08a3d931840328576de6b5ddc286f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "38e4a192d4a14f17bf5755396e07be04": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "39438991969a4c2596c705bb19120b26": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_17920dc032a04fb9968a89d20e84f29f"
       ],
       "layout": "IPY_MODEL_6e27eb0736854762bd9faf871f37101a"
      }
     },
     "3a66f369ff3b405294680bc2bfb122b9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_9d07892fd94a427fbaa44e874fa33742"
       ],
       "layout": "IPY_MODEL_d7fbf814b8f44752b39e45365886d09e"
      }
     },
     "3b82a06bdf3a4cb29c3843536a9b1920": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8b09382cd8964af0b7b6f22ad25791f6",
       "outputs": [
        {
         "data": {
          "image/png": "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\n",
          "text/plain": "<Figure size 720x720 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "3bffa8c29ff04d2a8550b441e60626c0": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_4b77001efb1f45f7883a9188746e0790",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of features?\n- We keep features only for leaf cells, a total of 56.\n- The number of leaf cells can be obtained by summarizing the \"1\" bits at level 3,\n  the last level of the octree.\n- The dimensionality of each attribute is 3 (e.g: RGB channels)\n\nReminder - the highest level of occupancy octree is:\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m-\u001B[32m8\u001B[0m,    01111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m-\u001B[32m7\u001B[0m,    10111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m-\u001B[32m6\u001B[0m,    11011111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m-\u001B[32m5\u001B[0m,    11101111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m-\u001B[32m4\u001B[0m,    11110111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m-\u001B[32m3\u001B[0m,    11111011\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m-\u001B[32m2\u001B[0m,    11111101\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m-\u001B[32m1\u001B[0m,    11111110\n"
        }
       ]
      }
     },
     "3d038715546c4acb9d976f3d90b68cfa": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_f3585de08e7b49df8d257fadfb5d2018",
       "outputs": [
        {
         "data": {
          "image/png": "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\n",
          "text/plain": "<Figure size 720x720 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "3d8801af180740bda3b50944016d9048": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "3df9a1ff615f4149b27de1e1b4e01c42": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "3ec6392fd9744909a7cd9436cc9ade06": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "404cc8e5336f4f84b87bea98a1668c5d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_652db7beb4de4f8bb7293a297400a886",
        "IPY_MODEL_8cd22bc7d9de48ebb1390e1245f556a6"
       ],
       "layout": "IPY_MODEL_1e36d98825aa48cebdb7e67adcb836b2"
      }
     },
     "405baecfd7a34a088b8461558dc39321": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "height": "100%"
      }
     },
     "42438351b4074ab784c228ea9afadd58": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_3df9a1ff615f4149b27de1e1b4e01c42",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of octrees?\n- Each entry represents a single octree of 8 cells --> 8 bits.\n- The bit position determines the cell index, in Morton Order.\n- The bit value determines if the cell is occupied or not.\n- If a cell is occupied, an additional octree may be generated in the next level, up till level 3.\nFor example, an entry of 00000001 is a single level octree, where only the bottom-left most cell is occupied.\n"
        }
       ]
      }
     },
     "469b8dd85299406cb7f1a04c4e606571": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "474fd7a41e624015a4c52ba6799bfa7a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_c8be2d747aa94f8cb39fa15dc815111a",
        "IPY_MODEL_13d28d7ed6104281abec8943427dbcc6"
       ],
       "layout": "IPY_MODEL_2480e7f508cc421189a344986f3f8334"
      }
     },
     "47e91765f8394549be0d5dce522dd1de": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_20d27e9c5b564a34bc82fdb7922b6725",
        "IPY_MODEL_bd57a21f5d1a4f608a7cd96b413e64df"
       ],
       "layout": "IPY_MODEL_9b70159d5a9e4104adf914addd1be4cb"
      }
     },
     "480c02b1ec184872bc0a40bb97a8d3f6": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "4864b3429e944ed483e8778781466203": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_8e813eacbfa14a25ab971cdcd9864cb3",
        "IPY_MODEL_b317356bed0d45218e0a4957af7f52c5"
       ],
       "layout": "IPY_MODEL_ee61f0297eff4e5e9750156de8764cd7"
      }
     },
     "4a10fa3318714fd99e7235536766c63e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "4a1ce300101b433d8988be9785142afa": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_93fdf74680b94553a3defd2eac0c7032",
       "outputs": [
        {
         "data": {
          "image/png": "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\n",
          "text/plain": "<Figure size 720x720 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "4b09b597b0bc48bca1907f3291efb313": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_c1610ac8eda8468fb39a5d5bfd9b422c"
       ],
       "layout": "IPY_MODEL_ca4d1e7941684cf696bc019ad2759bbc"
      }
     },
     "4b77001efb1f45f7883a9188746e0790": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "4d3eadf8eacd44bebe2cf2877b8a9abe": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4f53ddd23dd24cd6a25c1d88d68ea137": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4f811b0fa5f046a195d92e20aef0336f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_5cecab1246b4488ebe0d9de42d6effae",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"octrees\" represents a hierarchy of 17 octree nodes.\nEach bit represents a cell occupancy:\n\nLevel \u001B[31m#1, \u001B[0mPath\u001B[31m*\u001B[0m,        11111111\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m,      10000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m,      01000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m,      00100000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m,      00010000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m,      00001000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m,      00000100\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m,      00000010\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m,      00000001\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m-\u001B[32m8\u001B[0m,    01111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m-\u001B[32m7\u001B[0m,    10111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m-\u001B[32m6\u001B[0m,    11011111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m-\u001B[32m5\u001B[0m,    11101111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m-\u001B[32m4\u001B[0m,    11110111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m-\u001B[32m3\u001B[0m,    11111011\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m-\u001B[32m2\u001B[0m,    11111101\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m-\u001B[32m1\u001B[0m,    11111110\n"
        }
       ]
      }
     },
     "513a337a35e343e08b9d24323e015dad": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "<h5>SPC Level:</h5>",
       "layout": "IPY_MODEL_624af9c1cce4475582e32b09aa295c4e",
       "max": 3,
       "min": 1,
       "orientation": "vertical",
       "style": "IPY_MODEL_8d3ac938bf61482287cc8db16d7e8925",
       "value": 3
      }
     },
     "51e7576aac5e45f2a58880c96e821c6d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_990f7578131d4250a64bc04e920b76a5"
       ],
       "layout": "IPY_MODEL_1806f9178c224e9892e73a3e45eee48c"
      }
     },
     "5454cd1ec7494c579ddc20c5356f44f7": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "59163a4df3454e458ad3061ec1ff2358": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "5a3c3e466a9d4d8091d6b679c5009e71": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_6a55a15b7fe74846a542033d7342f44f",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"octrees\" represents a hierarchy of 17 octree nodes.\nEach bit represents a cell occupancy:\n\nLevel \u001B[31m#1, \u001B[0mPath\u001B[31m*\u001B[0m,        11111111\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m,      10000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m,      01000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m,      00100000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m,      00010000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m,      00001000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m,      00000100\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m,      00000010\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m,      00000001\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m-\u001B[32m8\u001B[0m,    01111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m-\u001B[32m7\u001B[0m,    10111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m-\u001B[32m6\u001B[0m,    11011111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m-\u001B[32m5\u001B[0m,    11101111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m-\u001B[32m4\u001B[0m,    11110111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m-\u001B[32m3\u001B[0m,    11111011\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m-\u001B[32m2\u001B[0m,    11111101\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m-\u001B[32m1\u001B[0m,    11111110\n"
        }
       ]
      }
     },
     "5cecab1246b4488ebe0d9de42d6effae": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "5d6931c3625e4cfa91fa11a333146261": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "624af9c1cce4475582e32b09aa295c4e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "height": "100%"
      }
     },
     "62c65d19c08d4065838d75fd11d6ad96": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_80781aab25494943b67485b8b846c3a0",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of lengths?\n- This Spc stores a batch of 1 octrees.\n- The first octree is represented by 17 non-leaf cells.\n- Therefore the information of the first octree is kept in bytes 0-16 of the octrees field.\n"
        }
       ]
      }
     },
     "64575c9e0484489ba61817b974956cac": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "646cd219bcb049e883af3c8e260d97d2": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "64aa5e0897a44c2393e05d2cee3baa00": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "<h5>SPC Level:</h5>",
       "layout": "IPY_MODEL_29203be51e1b4e2fbd40acaa6b6a21e3",
       "max": 3,
       "min": 1,
       "orientation": "vertical",
       "style": "IPY_MODEL_2e5d3befe1fc4429b7acbffe8386ccd1",
       "value": 3
      }
     },
     "64e1e62a6db6459a803ed1b650997110": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_1b0d2b69f50949d6ba0931eaae9ea654",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of octrees?\n- Each entry represents a single octree of 8 cells --> 8 bits.\n- The bit position determines the cell index, in Morton Order.\n- The bit value determines if the cell is occupied or not.\n- If a cell is occupied, an additional octree may be generated in the next level, up till level 3.\nFor example, an entry of 00000001 is a single level octree, where only the bottom-left most cell is occupied.\n"
        }
       ]
      }
     },
     "6510d56def394017b43995fd4a16b68d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "652db7beb4de4f8bb7293a297400a886": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_646cd219bcb049e883af3c8e260d97d2",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\u001B[30mLevel #0, Point #0, Coords: [0, 0, 0]\u001B[0m\n\u001B[31mLevel #1, Point #0, Coords: [0, 0, 0]\u001B[0m\n\u001B[31mLevel #1, Point #1, Coords: [0, 0, 1]\u001B[0m\n\u001B[31mLevel #1, Point #2, Coords: [0, 1, 0]\u001B[0m\n\u001B[31mLevel #1, Point #3, Coords: [0, 1, 1]\u001B[0m\n\u001B[31mLevel #1, Point #4, Coords: [1, 0, 0]\u001B[0m\n\u001B[31mLevel #1, Point #5, Coords: [1, 0, 1]\u001B[0m\n\u001B[31mLevel #1, Point #6, Coords: [1, 1, 0]\u001B[0m\n\u001B[31mLevel #1, Point #7, Coords: [1, 1, 1]\u001B[0m\n\u001B[31mLevel #1, Point #8, Coords: [1, 1, 1]\u001B[0m\n\u001B[34mLevel #2, Point #0, Coords: [1, 1, 2]\u001B[0m\n\u001B[34mLevel #2, Point #1, Coords: [1, 2, 1]\u001B[0m\n\u001B[34mLevel #2, Point #2, Coords: [1, 2, 2]\u001B[0m\n\u001B[34mLevel #2, Point #3, Coords: [2, 1, 1]\u001B[0m\n\u001B[34mLevel #2, Point #4, Coords: [2, 1, 2]\u001B[0m\n\u001B[34mLevel #2, Point #5, Coords: [2, 2, 1]\u001B[0m\n\u001B[34mLevel #2, Point #6, Coords: [2, 2, 2]\u001B[0m\n\u001B[34mLevel #2, Point #7, Coords: [2, 2, 2]\u001B[0m\n\u001B[34mLevel #2, Point #8, Coords: [2, 2, 3]\u001B[0m\n\u001B[32mLevel #3, Point #0, Coords: [2, 3, 2]\u001B[0m\n\u001B[32mLevel #3, Point #1, Coords: [2, 3, 3]\u001B[0m\n\u001B[32mLevel #3, Point #2, Coords: [3, 2, 2]\u001B[0m\n\u001B[32mLevel #3, Point #3, Coords: [3, 2, 3]\u001B[0m\n\u001B[32mLevel #3, Point #4, Coords: [3, 3, 2]\u001B[0m\n\u001B[32mLevel #3, Point #5, Coords: [2, 2, 4]\u001B[0m\n\u001B[32mLevel #3, Point #6, Coords: [2, 2, 5]\u001B[0m\n\u001B[32mLevel #3, Point #7, Coords: [2, 3, 4]\u001B[0m\n\u001B[32mLevel #3, Point #8, Coords: [2, 3, 5]\u001B[0m\n\u001B[32mLevel #3, Point #9, Coords: [3, 2, 4]\u001B[0m\n\u001B[32mLevel #3, Point #10, Coords: [3, 2, 5]\u001B[0m\n\u001B[32mLevel #3, Point #11, Coords: [3, 3, 5]\u001B[0m\n\u001B[32mLevel #3, Point #12, Coords: [2, 4, 2]\u001B[0m\n\u001B[32mLevel #3, Point #13, Coords: [2, 4, 3]\u001B[0m\n\u001B[32mLevel #3, Point #14, Coords: [2, 5, 2]\u001B[0m\n\u001B[32mLevel #3, Point #15, Coords: [2, 5, 3]\u001B[0m\n\u001B[32mLevel #3, Point #16, Coords: [3, 4, 2]\u001B[0m\n\u001B[32mskipping more..\u001B[0m\n"
        }
       ]
      }
     },
     "66a4e262c16c41d1b79e72914f4adbb1": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_1261aedad7f44d9594d480f57ef1622e",
       "outputs": [
        {
         "data": {
          "image/png": "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\n",
          "text/plain": "<Figure size 720x720 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "672e7a39c4f84898a31b67a349bfb23b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "69ad850155d04594a02f08dda622eb56": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_62c65d19c08d4065838d75fd11d6ad96"
       ],
       "layout": "IPY_MODEL_69e6df6e50654157b97a899dd330645a"
      }
     },
     "69e6df6e50654157b97a899dd330645a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "6a55a15b7fe74846a542033d7342f44f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "6a636935b26c4c60993583b5e8d53029": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_37a2f184b50e4bb9869dacc1c30cb876",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"exsum\" summarizes the cumulative number of occupied \ncells per octree, e.g: exclusive sum of \"1\" bits:\n\nCells in Octree #0 start from cell idx: 0\nCells in Octree #1 start from cell idx: 8\nCells in Octree #2 start from cell idx: 9\nCells in Octree #3 start from cell idx: 10\nCells in Octree #4 start from cell idx: 11\nCells in Octree #5 start from cell idx: 12\nCells in Octree #6 start from cell idx: 13\nCells in Octree #7 start from cell idx: 14\nCells in Octree #8 start from cell idx: 15\nCells in Octree #9 start from cell idx: 16\nCells in Octree #10 start from cell idx: 23\nCells in Octree #11 start from cell idx: 30\nCells in Octree #12 start from cell idx: 37\nCells in Octree #13 start from cell idx: 44\nCells in Octree #14 start from cell idx: 51\nCells in Octree #15 start from cell idx: 58\nCells in Octree #16 start from cell idx: 65\nCells in Octree #17 start from cell idx: 72\n"
        }
       ]
      }
     },
     "6ca65f5188c14df8955d21613ba1195c": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_22dde222a27b42a0875b5395d3ef98f3",
       "outputs": [
        {
         "data": {
          "image/png": "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\n",
          "text/plain": "<Figure size 720x720 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "6db5513817664352b82e1fe1267a2ee8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black",
       "width": "60%"
      }
     },
     "6e27eb0736854762bd9faf871f37101a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "71d47faef81e440b91fcc3374a12eccb": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "height": "100%"
      }
     },
     "7358d7bc25ed497e96f3c60e50777092": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_79ecf3b8909b46d5ac22589a218b7030",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of point_hierarchies?\n- Each cell / point is represented by 3 indices (xyz).\n- Sparse coordinates are absolute: \n  they are defined relative to the octree origin.\n- Compare the point coordinates with the demo below.\n\n  Remember: unoccupied cells are not displayed!\n"
        },
        {
         "data": {
          "application/vnd.jupyter.widget-view+json": {
           "model_id": "f2ae2f54d1694ea0baed809823bca1b6",
           "version_major": 2,
           "version_minor": 0
          },
          "text/plain": "HBox(children=(VBox(children=(IntSlider(value=3, description='<h5>SPC Level:</h5>', layout=Layout(height='100%…"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "7534e08049c645fd9953e91b0abd0b8f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_0eb89324c3944e1db2baf3171df1773f",
        "IPY_MODEL_a6d291695268475b98eb8f2914863c88"
       ],
       "layout": "IPY_MODEL_e9513adb65ca4595bfdf9d31cee4153a"
      }
     },
     "75aefa29316944a88b9bea3cab00f0dd": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "78b504385e8243a69ed54053b2507e74": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "height": "100%"
      }
     },
     "79ecf3b8909b46d5ac22589a218b7030": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black",
       "width": "60%"
      }
     },
     "7bd49d6189154d62afc3984e530d25ec": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "<h5>SPC Level:</h5>",
       "layout": "IPY_MODEL_2b4b9f95c4a6426ea60e5080d2b07b31",
       "max": 10,
       "min": 1,
       "orientation": "vertical",
       "style": "IPY_MODEL_b3a3d10b9f1044159f29f7fb2105c04c",
       "value": 7
      }
     },
     "7dc80b9b8f3142318a6b6f1cd79fc43d": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_09275cc785ae4305b2e4f800b98bd6f3",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"octrees\" represents a hierarchy of 17 octree nodes.\nEach bit represents a cell occupancy:\n\nLevel \u001B[31m#1, \u001B[0mPath\u001B[31m*\u001B[0m,        11111111\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m,      10000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m,      01000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m,      00100000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m,      00010000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m,      00001000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m,      00000100\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m,      00000010\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m,      00000001\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m-\u001B[32m8\u001B[0m,    01111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m-\u001B[32m7\u001B[0m,    10111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m-\u001B[32m6\u001B[0m,    11011111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m-\u001B[32m5\u001B[0m,    11101111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m-\u001B[32m4\u001B[0m,    11110111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m-\u001B[32m3\u001B[0m,    11111011\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m-\u001B[32m2\u001B[0m,    11111101\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m-\u001B[32m1\u001B[0m,    11111110\n"
        }
       ]
      }
     },
     "7f7a570f2e20449fa382eb4694674817": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_64aa5e0897a44c2393e05d2cee3baa00"
       ],
       "layout": "IPY_MODEL_ed938282f5df4659a8bfae78f54ce43f"
      }
     },
     "80781aab25494943b67485b8b846c3a0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "80a6d52eb8594fe4a4c68f43ad0e89c0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "80ba4558ee1f40bcb891d1e905646ac8": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_480c02b1ec184872bc0a40bb97a8d3f6",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of lengths?\n- This Spc stores a batch of 1 octrees.\n- The first octree is represented by 17 non-leaf cells.\n- Therefore the information of the first octree is kept in bytes 0-16 of the octrees field.\n"
        }
       ]
      }
     },
     "80d81284075b4376a2357c5efd65a61f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "8321f8ce47c7451593b81873eace8383": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_d496092ae178423190b4b67b3f2e73de",
        "IPY_MODEL_fe521de6840b4e6e83f223978a491109"
       ],
       "layout": "IPY_MODEL_6510d56def394017b43995fd4a16b68d"
      }
     },
     "84ac8394ea914bdaafa3546a8c1db6a6": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_106014b2958a43f7986981054210a3f8",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\u001B[30mLevel #0, Point #0, Coords: [0, 0, 0]\u001B[0m\n\u001B[31mLevel #1, Point #0, Coords: [0, 0, 0]\u001B[0m\n\u001B[31mLevel #1, Point #1, Coords: [0, 0, 1]\u001B[0m\n\u001B[31mLevel #1, Point #2, Coords: [0, 1, 0]\u001B[0m\n\u001B[31mLevel #1, Point #3, Coords: [0, 1, 1]\u001B[0m\n\u001B[31mLevel #1, Point #4, Coords: [1, 0, 0]\u001B[0m\n\u001B[31mLevel #1, Point #5, Coords: [1, 0, 1]\u001B[0m\n\u001B[31mLevel #1, Point #6, Coords: [1, 1, 0]\u001B[0m\n\u001B[31mLevel #1, Point #7, Coords: [1, 1, 1]\u001B[0m\n\u001B[31mLevel #1, Point #8, Coords: [1, 1, 1]\u001B[0m\n\u001B[34mLevel #2, Point #0, Coords: [1, 1, 2]\u001B[0m\n\u001B[34mLevel #2, Point #1, Coords: [1, 2, 1]\u001B[0m\n\u001B[34mLevel #2, Point #2, Coords: [1, 2, 2]\u001B[0m\n\u001B[34mLevel #2, Point #3, Coords: [2, 1, 1]\u001B[0m\n\u001B[34mLevel #2, Point #4, Coords: [2, 1, 2]\u001B[0m\n\u001B[34mLevel #2, Point #5, Coords: [2, 2, 1]\u001B[0m\n\u001B[34mLevel #2, Point #6, Coords: [2, 2, 2]\u001B[0m\n\u001B[34mLevel #2, Point #7, Coords: [2, 2, 2]\u001B[0m\n\u001B[34mLevel #2, Point #8, Coords: [2, 2, 3]\u001B[0m\n\u001B[32mLevel #3, Point #0, Coords: [2, 3, 2]\u001B[0m\n\u001B[32mLevel #3, Point #1, Coords: [2, 3, 3]\u001B[0m\n\u001B[32mLevel #3, Point #2, Coords: [3, 2, 2]\u001B[0m\n\u001B[32mLevel #3, Point #3, Coords: [3, 2, 3]\u001B[0m\n\u001B[32mLevel #3, Point #4, Coords: [3, 3, 2]\u001B[0m\n\u001B[32mLevel #3, Point #5, Coords: [2, 2, 4]\u001B[0m\n\u001B[32mLevel #3, Point #6, Coords: [2, 2, 5]\u001B[0m\n\u001B[32mLevel #3, Point #7, Coords: [2, 3, 4]\u001B[0m\n\u001B[32mLevel #3, Point #8, Coords: [2, 3, 5]\u001B[0m\n\u001B[32mLevel #3, Point #9, Coords: [3, 2, 4]\u001B[0m\n\u001B[32mLevel #3, Point #10, Coords: [3, 2, 5]\u001B[0m\n\u001B[32mLevel #3, Point #11, Coords: [3, 3, 5]\u001B[0m\n\u001B[32mLevel #3, Point #12, Coords: [2, 4, 2]\u001B[0m\n\u001B[32mLevel #3, Point #13, Coords: [2, 4, 3]\u001B[0m\n\u001B[32mLevel #3, Point #14, Coords: [2, 5, 2]\u001B[0m\n\u001B[32mLevel #3, Point #15, Coords: [2, 5, 3]\u001B[0m\n\u001B[32mLevel #3, Point #16, Coords: [3, 4, 2]\u001B[0m\n\u001B[32mskipping more..\u001B[0m\n"
        }
       ]
      }
     },
     "85450606f352421e80a37a6c6c70a8d5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "8825742944a444e080e1b23e8c061b50": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "height": "100%"
      }
     },
     "8a27344de8394a3ca35016c262d4b11d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_6a636935b26c4c60993583b5e8d53029",
        "IPY_MODEL_7dc80b9b8f3142318a6b6f1cd79fc43d"
       ],
       "layout": "IPY_MODEL_c8c46f8a54e24594999e50d66caaf914"
      }
     },
     "8b09382cd8964af0b7b6f22ad25791f6": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "8cd22bc7d9de48ebb1390e1245f556a6": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_6db5513817664352b82e1fe1267a2ee8",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of point_hierarchies?\n- Each cell / point is represented by 3 indices (xyz).\n- Sparse coordinates are absolute: \n  they are defined relative to the octree origin.\n- Compare the point coordinates with the demo below.\n\n  Remember: unoccupied cells are not displayed!\n"
        },
        {
         "data": {
          "application/vnd.jupyter.widget-view+json": {
           "model_id": "e7fc92253f504c4b800574a69a7f93c5",
           "version_major": 2,
           "version_minor": 0
          },
          "text/plain": "HBox(children=(VBox(children=(IntSlider(value=3, description='<h5>SPC Level:</h5>', layout=Layout(height='100%…"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "8d3ac938bf61482287cc8db16d7e8925": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "8d87b221feab40a5a01f1e5a66a5b22c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_8fda9c06ac7f4ca09c4ff9331f573075",
        "IPY_MODEL_17acfce4e50c4221b48e7f404c185b61"
       ],
       "layout": "IPY_MODEL_75aefa29316944a88b9bea3cab00f0dd"
      }
     },
     "8db7baa892894abea05879b3d6e58ca3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black",
       "width": "60%"
      }
     },
     "8dd7df7c079740fda41cf3dc3274adb0": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "8e813eacbfa14a25ab971cdcd9864cb3": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_f0cdec2c173f4710809ed6bda21d98fe",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\u001B[30mLevel #0, Point #0, Coords: [0, 0, 0]\u001B[0m\n\u001B[31mLevel #1, Point #0, Coords: [0, 0, 0]\u001B[0m\n\u001B[31mLevel #1, Point #1, Coords: [0, 0, 1]\u001B[0m\n\u001B[31mLevel #1, Point #2, Coords: [0, 1, 0]\u001B[0m\n\u001B[31mLevel #1, Point #3, Coords: [0, 1, 1]\u001B[0m\n\u001B[31mLevel #1, Point #4, Coords: [1, 0, 0]\u001B[0m\n\u001B[31mLevel #1, Point #5, Coords: [1, 0, 1]\u001B[0m\n\u001B[31mLevel #1, Point #6, Coords: [1, 1, 0]\u001B[0m\n\u001B[31mLevel #1, Point #7, Coords: [1, 1, 1]\u001B[0m\n\u001B[31mLevel #1, Point #8, Coords: [1, 1, 1]\u001B[0m\n\u001B[34mLevel #2, Point #0, Coords: [1, 1, 2]\u001B[0m\n\u001B[34mLevel #2, Point #1, Coords: [1, 2, 1]\u001B[0m\n\u001B[34mLevel #2, Point #2, Coords: [1, 2, 2]\u001B[0m\n\u001B[34mLevel #2, Point #3, Coords: [2, 1, 1]\u001B[0m\n\u001B[34mLevel #2, Point #4, Coords: [2, 1, 2]\u001B[0m\n\u001B[34mLevel #2, Point #5, Coords: [2, 2, 1]\u001B[0m\n\u001B[34mLevel #2, Point #6, Coords: [2, 2, 2]\u001B[0m\n\u001B[34mLevel #2, Point #7, Coords: [2, 2, 2]\u001B[0m\n\u001B[34mLevel #2, Point #8, Coords: [2, 2, 3]\u001B[0m\n\u001B[32mLevel #3, Point #0, Coords: [2, 3, 2]\u001B[0m\n\u001B[32mLevel #3, Point #1, Coords: [2, 3, 3]\u001B[0m\n\u001B[32mLevel #3, Point #2, Coords: [3, 2, 2]\u001B[0m\n\u001B[32mLevel #3, Point #3, Coords: [3, 2, 3]\u001B[0m\n\u001B[32mLevel #3, Point #4, Coords: [3, 3, 2]\u001B[0m\n\u001B[32mLevel #3, Point #5, Coords: [2, 2, 4]\u001B[0m\n\u001B[32mLevel #3, Point #6, Coords: [2, 2, 5]\u001B[0m\n\u001B[32mLevel #3, Point #7, Coords: [2, 3, 4]\u001B[0m\n\u001B[32mLevel #3, Point #8, Coords: [2, 3, 5]\u001B[0m\n\u001B[32mLevel #3, Point #9, Coords: [3, 2, 4]\u001B[0m\n\u001B[32mLevel #3, Point #10, Coords: [3, 2, 5]\u001B[0m\n\u001B[32mLevel #3, Point #11, Coords: [3, 3, 5]\u001B[0m\n\u001B[32mLevel #3, Point #12, Coords: [2, 4, 2]\u001B[0m\n\u001B[32mLevel #3, Point #13, Coords: [2, 4, 3]\u001B[0m\n\u001B[32mLevel #3, Point #14, Coords: [2, 5, 2]\u001B[0m\n\u001B[32mLevel #3, Point #15, Coords: [2, 5, 3]\u001B[0m\n\u001B[32mLevel #3, Point #16, Coords: [3, 4, 2]\u001B[0m\n\u001B[32mskipping more..\u001B[0m\n"
        }
       ]
      }
     },
     "8ee9fdd3e20b4c23ae32ecfba3744dc6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_e4a1d8d751ad41409ea6309744f84cb1"
       ],
       "layout": "IPY_MODEL_5d6931c3625e4cfa91fa11a333146261"
      }
     },
     "8f96bac864e34ceca79d4fcc9c4148ac": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "8f9f30c9b96d4a308f74dd3127cf9f8f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "8fda9c06ac7f4ca09c4ff9331f573075": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_9baacfab3ff046e4bbc583970222bf4e",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"pyramid\" summarizes the number of occupied \ncells per level, and their cumulative sum:\n\nRoot node (implicitly defined):\n\thas 1 occupied cells\n\tstart idx (cumsum excluding current level): 0\nLevel #1:\n\thas 8 occupied cells\n\tstart idx (cumsum excluding current level): 1\nLevel #2:\n\thas 8 occupied cells\n\tstart idx (cumsum excluding current level): 9\nLevel #3:\n\thas 56 occupied cells\n\tstart idx (cumsum excluding current level): 17\nFinal entry for total cumsum:\n\thas 0 occupied cells\n\tstart idx (cumsum excluding current level): 73\n"
        }
       ]
      }
     },
     "902b32f6283c44e8a400a4bbb1485fb6": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "9287187f32d3479f9d100385054f0023": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "93fdf74680b94553a3defd2eac0c7032": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "990f7578131d4250a64bc04e920b76a5": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_3ec6392fd9744909a7cd9436cc9ade06",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of features?\n- We keep features only for leaf cells, a total of 56.\n- The number of leaf cells can be obtained by summarizing the \"1\" bits at level 3,\n  the last level of the octree.\n- The dimensionality of each attribute is 3 (e.g: RGB channels)\n\nReminder - the highest level of occupancy octree is:\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m-\u001B[32m8\u001B[0m,    01111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m-\u001B[32m7\u001B[0m,    10111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m-\u001B[32m6\u001B[0m,    11011111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m-\u001B[32m5\u001B[0m,    11101111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m-\u001B[32m4\u001B[0m,    11110111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m-\u001B[32m3\u001B[0m,    11111011\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m-\u001B[32m2\u001B[0m,    11111101\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m-\u001B[32m1\u001B[0m,    11111110\n"
        }
       ]
      }
     },
     "9a12caa20fee4d79be6fbb5aeb77e0ba": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_84ac8394ea914bdaafa3546a8c1db6a6",
        "IPY_MODEL_a16d8f93e75745bc9eb8562e16c91535"
       ],
       "layout": "IPY_MODEL_185c5cb6233949dc8804860622bd17e6"
      }
     },
     "9b70159d5a9e4104adf914addd1be4cb": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "9baacfab3ff046e4bbc583970222bf4e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "9c95a78eb2fa4fbebae584cc574af4c3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_cea72f6bf7224b789cf40e4df04c49d0"
       ],
       "layout": "IPY_MODEL_07ce6ab72e814e2f885d200054d8c2b0"
      }
     },
     "9d07892fd94a427fbaa44e874fa33742": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_902b32f6283c44e8a400a4bbb1485fb6",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of features?\n- We keep features only for leaf cells, a total of 56.\n- The number of leaf cells can be obtained by summarizing the \"1\" bits at level 3,\n  the last level of the octree.\n- The dimensionality of each attribute is 3 (e.g: RGB channels)\n\nReminder - the highest level of occupancy octree is:\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m-\u001B[32m8\u001B[0m,    01111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m-\u001B[32m7\u001B[0m,    10111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m-\u001B[32m6\u001B[0m,    11011111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m-\u001B[32m5\u001B[0m,    11101111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m-\u001B[32m4\u001B[0m,    11110111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m-\u001B[32m3\u001B[0m,    11111011\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m-\u001B[32m2\u001B[0m,    11111101\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m-\u001B[32m1\u001B[0m,    11111110\n"
        }
       ]
      }
     },
     "9f8d79002e3644f792959def1eabbc34": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "9f9a4a713bd94238b1afe374e4202390": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_dfa841b8d4ee4abdba245cb7436391f5",
        "IPY_MODEL_5a3c3e466a9d4d8091d6b679c5009e71"
       ],
       "layout": "IPY_MODEL_8dd7df7c079740fda41cf3dc3274adb0"
      }
     },
     "a0f5f3be261f4f8cba843d28fc222c59": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "a16d8f93e75745bc9eb8562e16c91535": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_24e36745221b4d9cb213262eb7019f9b",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of point_hierarchies?\n- Each cell / point is represented by 3 indices (xyz).\n- Sparse coordinates are absolute: \n  they are defined relative to the octree origin.\n- Compare the point coordinates with the demo below.\n\n  Remember: unoccupied cells are not displayed!\n"
        },
        {
         "data": {
          "application/vnd.jupyter.widget-view+json": {
           "model_id": "7534e08049c645fd9953e91b0abd0b8f",
           "version_major": 2,
           "version_minor": 0
          },
          "text/plain": "HBox(children=(VBox(children=(IntSlider(value=3, description='<h5>SPC Level:</h5>', layout=Layout(height='100%…"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "a3c0e0ce666341a8bd1bc00957d5cb18": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "a6d291695268475b98eb8f2914863c88": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_a3c0e0ce666341a8bd1bc00957d5cb18",
       "outputs": [
        {
         "data": {
          "image/png": "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\n",
          "text/plain": "<Figure size 720x720 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "a7954d65e5f64d2f853cdc844574dd0c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_25f2ac5125874b719458a773e3d1a301",
        "IPY_MODEL_df88b9a94dc9402fadc8fa0f13177a3f"
       ],
       "layout": "IPY_MODEL_4d3eadf8eacd44bebe2cf2877b8a9abe"
      }
     },
     "a7f2e4c4a30b4181b26d9eb2c7e32902": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_e74512a2c7c643b698c71b432b076be3",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"octrees\" represents a hierarchy of 17 octree nodes.\nEach bit represents a cell occupancy:\n\nLevel \u001B[31m#1, \u001B[0mPath\u001B[31m*\u001B[0m,        11111111\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m,      10000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m,      01000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m,      00100000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m,      00010000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m,      00001000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m,      00000100\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m,      00000010\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m,      00000001\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m-\u001B[32m8\u001B[0m,    01111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m-\u001B[32m7\u001B[0m,    10111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m-\u001B[32m6\u001B[0m,    11011111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m-\u001B[32m5\u001B[0m,    11101111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m-\u001B[32m4\u001B[0m,    11110111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m-\u001B[32m3\u001B[0m,    11111011\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m-\u001B[32m2\u001B[0m,    11111101\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m-\u001B[32m1\u001B[0m,    11111110\n"
        }
       ]
      }
     },
     "abfbd5d5687443708d121c9841af086b": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_b01da83cd1474c61be8f2e2986c9c8b5",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"octrees\" represents a hierarchy of 17 octree nodes.\nEach bit represents a cell occupancy:\n\nLevel \u001B[31m#1, \u001B[0mPath\u001B[31m*\u001B[0m,        11111111\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m,      10000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m,      01000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m,      00100000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m,      00010000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m,      00001000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m,      00000100\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m,      00000010\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m,      00000001\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m-\u001B[32m8\u001B[0m,    01111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m-\u001B[32m7\u001B[0m,    10111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m-\u001B[32m6\u001B[0m,    11011111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m-\u001B[32m5\u001B[0m,    11101111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m-\u001B[32m4\u001B[0m,    11110111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m-\u001B[32m3\u001B[0m,    11111011\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m-\u001B[32m2\u001B[0m,    11111101\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m-\u001B[32m1\u001B[0m,    11111110\n"
        }
       ]
      }
     },
     "afb2c576e1a34574b61424fbfb4ff779": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b01da83cd1474c61be8f2e2986c9c8b5": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "b0ba3b72b8764ea380f2e9f9e1b168d4": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_469b8dd85299406cb7f1a04c4e606571",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"pyramid\" summarizes the number of occupied \ncells per level, and their cumulative sum:\n\nRoot node (implicitly defined):\n\thas 1 occupied cells\n\tstart idx (cumsum excluding current level): 0\nLevel #1:\n\thas 8 occupied cells\n\tstart idx (cumsum excluding current level): 1\nLevel #2:\n\thas 8 occupied cells\n\tstart idx (cumsum excluding current level): 9\nLevel #3:\n\thas 56 occupied cells\n\tstart idx (cumsum excluding current level): 17\nFinal entry for total cumsum:\n\thas 0 occupied cells\n\tstart idx (cumsum excluding current level): 73\n"
        }
       ]
      }
     },
     "b26f6ad199214573998cac894f17a404": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "b2f3a2e38a454f9f95645d2f34a101ee": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_cf758a0970e44ff5bcc98a210169e498",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\u001B[30mLevel #0, Point #0, Coords: [0, 0, 0]\u001B[0m\n\u001B[31mLevel #1, Point #0, Coords: [0, 0, 0]\u001B[0m\n\u001B[31mLevel #1, Point #1, Coords: [0, 0, 1]\u001B[0m\n\u001B[31mLevel #1, Point #2, Coords: [0, 1, 0]\u001B[0m\n\u001B[31mLevel #1, Point #3, Coords: [0, 1, 1]\u001B[0m\n\u001B[31mLevel #1, Point #4, Coords: [1, 0, 0]\u001B[0m\n\u001B[31mLevel #1, Point #5, Coords: [1, 0, 1]\u001B[0m\n\u001B[31mLevel #1, Point #6, Coords: [1, 1, 0]\u001B[0m\n\u001B[31mLevel #1, Point #7, Coords: [1, 1, 1]\u001B[0m\n\u001B[31mLevel #1, Point #8, Coords: [1, 1, 1]\u001B[0m\n\u001B[34mLevel #2, Point #0, Coords: [1, 1, 2]\u001B[0m\n\u001B[34mLevel #2, Point #1, Coords: [1, 2, 1]\u001B[0m\n\u001B[34mLevel #2, Point #2, Coords: [1, 2, 2]\u001B[0m\n\u001B[34mLevel #2, Point #3, Coords: [2, 1, 1]\u001B[0m\n\u001B[34mLevel #2, Point #4, Coords: [2, 1, 2]\u001B[0m\n\u001B[34mLevel #2, Point #5, Coords: [2, 2, 1]\u001B[0m\n\u001B[34mLevel #2, Point #6, Coords: [2, 2, 2]\u001B[0m\n\u001B[34mLevel #2, Point #7, Coords: [2, 2, 2]\u001B[0m\n\u001B[34mLevel #2, Point #8, Coords: [2, 2, 3]\u001B[0m\n\u001B[32mLevel #3, Point #0, Coords: [2, 3, 2]\u001B[0m\n\u001B[32mLevel #3, Point #1, Coords: [2, 3, 3]\u001B[0m\n\u001B[32mLevel #3, Point #2, Coords: [3, 2, 2]\u001B[0m\n\u001B[32mLevel #3, Point #3, Coords: [3, 2, 3]\u001B[0m\n\u001B[32mLevel #3, Point #4, Coords: [3, 3, 2]\u001B[0m\n\u001B[32mLevel #3, Point #5, Coords: [2, 2, 4]\u001B[0m\n\u001B[32mLevel #3, Point #6, Coords: [2, 2, 5]\u001B[0m\n\u001B[32mLevel #3, Point #7, Coords: [2, 3, 4]\u001B[0m\n\u001B[32mLevel #3, Point #8, Coords: [2, 3, 5]\u001B[0m\n\u001B[32mLevel #3, Point #9, Coords: [3, 2, 4]\u001B[0m\n\u001B[32mLevel #3, Point #10, Coords: [3, 2, 5]\u001B[0m\n\u001B[32mLevel #3, Point #11, Coords: [3, 3, 5]\u001B[0m\n\u001B[32mLevel #3, Point #12, Coords: [2, 4, 2]\u001B[0m\n\u001B[32mLevel #3, Point #13, Coords: [2, 4, 3]\u001B[0m\n\u001B[32mLevel #3, Point #14, Coords: [2, 5, 2]\u001B[0m\n\u001B[32mLevel #3, Point #15, Coords: [2, 5, 3]\u001B[0m\n\u001B[32mLevel #3, Point #16, Coords: [3, 4, 2]\u001B[0m\n\u001B[32mskipping more..\u001B[0m\n"
        }
       ]
      }
     },
     "b317356bed0d45218e0a4957af7f52c5": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8db7baa892894abea05879b3d6e58ca3",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of point_hierarchies?\n- Each cell / point is represented by 3 indices (xyz).\n- Sparse coordinates are absolute: \n  they are defined relative to the octree origin.\n- Compare the point coordinates with the demo below.\n\n  Remember: unoccupied cells are not displayed!\n"
        },
        {
         "data": {
          "application/vnd.jupyter.widget-view+json": {
           "model_id": "47e91765f8394549be0d5dce522dd1de",
           "version_major": 2,
           "version_minor": 0
          },
          "text/plain": "HBox(children=(VBox(children=(IntSlider(value=3, description='<h5>SPC Level:</h5>', layout=Layout(height='100%…"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "b3a3d10b9f1044159f29f7fb2105c04c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "b405637957e646a8b34e32b616bceebb": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "b6466ea18c6b4e9f861a19c214866beb": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_d27a640fe214437e8861e39f4ce568ed"
       ],
       "layout": "IPY_MODEL_d8c2e2fe347d41cdb21c6e25106385e4"
      }
     },
     "b6e69cdc5f7646788740bd53af7f0c13": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_0dba0870896341a3b4a8b92f7b49db17",
        "IPY_MODEL_3d038715546c4acb9d976f3d90b68cfa"
       ],
       "layout": "IPY_MODEL_9f8d79002e3644f792959def1eabbc34"
      }
     },
     "b75d3608c36b489fb0311c996f7ab57e": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_5454cd1ec7494c579ddc20c5356f44f7",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of features?\n- We keep features only for leaf cells, a total of 56.\n- The number of leaf cells can be obtained by summarizing the \"1\" bits at level 3,\n  the last level of the octree.\n- The dimensionality of each attribute is 3 (e.g: RGB channels)\n\nReminder - the highest level of occupancy octree is:\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m-\u001B[32m8\u001B[0m,    01111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m-\u001B[32m7\u001B[0m,    10111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m-\u001B[32m6\u001B[0m,    11011111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m-\u001B[32m5\u001B[0m,    11101111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m-\u001B[32m4\u001B[0m,    11110111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m-\u001B[32m3\u001B[0m,    11111011\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m-\u001B[32m2\u001B[0m,    11111101\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m-\u001B[32m1\u001B[0m,    11111110\n"
        }
       ]
      }
     },
     "b9f68c2591da4595ac0f9ede6aba0947": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "ba433165e9ec4dbc96fb694414456010": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "<h5>SPC Level:</h5>",
       "layout": "IPY_MODEL_71d47faef81e440b91fcc3374a12eccb",
       "max": 10,
       "min": 1,
       "orientation": "vertical",
       "style": "IPY_MODEL_fcf3e5b018cc497291c1b442be8d1057",
       "value": 7
      }
     },
     "bb647370ace741e68ed37756cc10b4df": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "height": "100%"
      }
     },
     "bd57a21f5d1a4f608a7cd96b413e64df": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_173afffaeaf64066b6eb773580eee392",
       "outputs": [
        {
         "data": {
          "image/png": "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\n",
          "text/plain": "<Figure size 720x720 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "bdddb4b67c1d40fdbc75efc4dc4f240c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "be36847bf97f490c91a204c6c1fa66bb": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "<h5>SPC Level:</h5>",
       "layout": "IPY_MODEL_78b504385e8243a69ed54053b2507e74",
       "max": 3,
       "min": 1,
       "orientation": "vertical",
       "style": "IPY_MODEL_80d81284075b4376a2357c5efd65a61f",
       "value": 3
      }
     },
     "bf825adf51634d38bcfa66fda33d3eb7": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_3bffa8c29ff04d2a8550b441e60626c0"
       ],
       "layout": "IPY_MODEL_afb2c576e1a34574b61424fbfb4ff779"
      }
     },
     "c06f4c8abfda4a548e2c254b62814a71": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_b405637957e646a8b34e32b616bceebb",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"pyramid\" summarizes the number of occupied \ncells per level, and their cumulative sum:\n\nRoot node (implicitly defined):\n\thas 1 occupied cells\n\tstart idx (cumsum excluding current level): 0\nLevel #1:\n\thas 8 occupied cells\n\tstart idx (cumsum excluding current level): 1\nLevel #2:\n\thas 8 occupied cells\n\tstart idx (cumsum excluding current level): 9\nLevel #3:\n\thas 56 occupied cells\n\tstart idx (cumsum excluding current level): 17\nFinal entry for total cumsum:\n\thas 0 occupied cells\n\tstart idx (cumsum excluding current level): 73\n"
        }
       ]
      }
     },
     "c1610ac8eda8468fb39a5d5bfd9b422c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "<h5>SPC Level:</h5>",
       "layout": "IPY_MODEL_8825742944a444e080e1b23e8c061b50",
       "max": 10,
       "min": 1,
       "orientation": "vertical",
       "style": "IPY_MODEL_37e08a3d931840328576de6b5ddc286f",
       "value": 7
      }
     },
     "c2f06a77b15c4912a79917714ae84cd7": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_9287187f32d3479f9d100385054f0023",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"exsum\" summarizes the cumulative number of occupied \ncells per octree, e.g: exclusive sum of \"1\" bits:\n\nCells in Octree #0 start from cell idx: 0\nCells in Octree #1 start from cell idx: 8\nCells in Octree #2 start from cell idx: 9\nCells in Octree #3 start from cell idx: 10\nCells in Octree #4 start from cell idx: 11\nCells in Octree #5 start from cell idx: 12\nCells in Octree #6 start from cell idx: 13\nCells in Octree #7 start from cell idx: 14\nCells in Octree #8 start from cell idx: 15\nCells in Octree #9 start from cell idx: 16\nCells in Octree #10 start from cell idx: 23\nCells in Octree #11 start from cell idx: 30\nCells in Octree #12 start from cell idx: 37\nCells in Octree #13 start from cell idx: 44\nCells in Octree #14 start from cell idx: 51\nCells in Octree #15 start from cell idx: 58\nCells in Octree #16 start from cell idx: 65\nCells in Octree #17 start from cell idx: 72\n"
        }
       ]
      }
     },
     "c421b4564aea49d88d01d91da8a64915": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_64e1e62a6db6459a803ed1b650997110"
       ],
       "layout": "IPY_MODEL_64575c9e0484489ba61817b974956cac"
      }
     },
     "c6e8f7597031435ea093e3c5966f274b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "c8be2d747aa94f8cb39fa15dc815111a": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_e2377a62bf694ef7956af10badc4e53e",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"exsum\" summarizes the cumulative number of occupied \ncells per octree, e.g: exclusive sum of \"1\" bits:\n\nCells in Octree #0 start from cell idx: 0\nCells in Octree #1 start from cell idx: 8\nCells in Octree #2 start from cell idx: 9\nCells in Octree #3 start from cell idx: 10\nCells in Octree #4 start from cell idx: 11\nCells in Octree #5 start from cell idx: 12\nCells in Octree #6 start from cell idx: 13\nCells in Octree #7 start from cell idx: 14\nCells in Octree #8 start from cell idx: 15\nCells in Octree #9 start from cell idx: 16\nCells in Octree #10 start from cell idx: 23\nCells in Octree #11 start from cell idx: 30\nCells in Octree #12 start from cell idx: 37\nCells in Octree #13 start from cell idx: 44\nCells in Octree #14 start from cell idx: 51\nCells in Octree #15 start from cell idx: 58\nCells in Octree #16 start from cell idx: 65\nCells in Octree #17 start from cell idx: 72\n"
        }
       ]
      }
     },
     "c8c46f8a54e24594999e50d66caaf914": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "ca4d1e7941684cf696bc019ad2759bbc": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "cea72f6bf7224b789cf40e4df04c49d0": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_e132d3014a3d462685b10ff93debcf5e",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of octrees?\n- Each entry represents a single octree of 8 cells --> 8 bits.\n- The bit position determines the cell index, in Morton Order.\n- The bit value determines if the cell is occupied or not.\n- If a cell is occupied, an additional octree may be generated in the next level, up till level 3.\nFor example, an entry of 00000001 is a single level octree, where only the bottom-left most cell is occupied.\n"
        }
       ]
      }
     },
     "cf758a0970e44ff5bcc98a210169e498": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "d27a640fe214437e8861e39f4ce568ed": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_1f4dde12efce436e89585750c8d07e78",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of lengths?\n- This Spc stores a batch of 1 octrees.\n- The first octree is represented by 17 non-leaf cells.\n- Therefore the information of the first octree is kept in bytes 0-16 of the octrees field.\n"
        }
       ]
      }
     },
     "d496092ae178423190b4b67b3f2e73de": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_1fab918b08a842f5a3752406485bf378",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"exsum\" summarizes the cumulative number of occupied \ncells per octree, e.g: exclusive sum of \"1\" bits:\n\nCells in Octree #0 start from cell idx: 0\nCells in Octree #1 start from cell idx: 8\nCells in Octree #2 start from cell idx: 9\nCells in Octree #3 start from cell idx: 10\nCells in Octree #4 start from cell idx: 11\nCells in Octree #5 start from cell idx: 12\nCells in Octree #6 start from cell idx: 13\nCells in Octree #7 start from cell idx: 14\nCells in Octree #8 start from cell idx: 15\nCells in Octree #9 start from cell idx: 16\nCells in Octree #10 start from cell idx: 23\nCells in Octree #11 start from cell idx: 30\nCells in Octree #12 start from cell idx: 37\nCells in Octree #13 start from cell idx: 44\nCells in Octree #14 start from cell idx: 51\nCells in Octree #15 start from cell idx: 58\nCells in Octree #16 start from cell idx: 65\nCells in Octree #17 start from cell idx: 72\n"
        }
       ]
      }
     },
     "d7fbf814b8f44752b39e45365886d09e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "d8c2e2fe347d41cdb21c6e25106385e4": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "df88b9a94dc9402fadc8fa0f13177a3f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_672e7a39c4f84898a31b67a349bfb23b",
       "outputs": [
        {
         "data": {
          "image/png": "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\n",
          "text/plain": "<Figure size 720x720 with 1 Axes>"
         },
         "metadata": {
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ]
      }
     },
     "dfa841b8d4ee4abdba245cb7436391f5": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_a0f5f3be261f4f8cba843d28fc222c59",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"pyramid\" summarizes the number of occupied \ncells per level, and their cumulative sum:\n\nRoot node (implicitly defined):\n\thas 1 occupied cells\n\tstart idx (cumsum excluding current level): 0\nLevel #1:\n\thas 8 occupied cells\n\tstart idx (cumsum excluding current level): 1\nLevel #2:\n\thas 8 occupied cells\n\tstart idx (cumsum excluding current level): 9\nLevel #3:\n\thas 56 occupied cells\n\tstart idx (cumsum excluding current level): 17\nFinal entry for total cumsum:\n\thas 0 occupied cells\n\tstart idx (cumsum excluding current level): 73\n"
        }
       ]
      }
     },
     "e132d3014a3d462685b10ff93debcf5e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "e2377a62bf694ef7956af10badc4e53e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "e47f51e2c3534fb382f514259f0146db": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_42438351b4074ab784c228ea9afadd58"
       ],
       "layout": "IPY_MODEL_e953e8cee6cb4085816e626147a79efc"
      }
     },
     "e4a1d8d751ad41409ea6309744f84cb1": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_06460c2c1d6741eab0636684e16022bc",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "How to read the content of octrees?\n- Each entry represents a single octree of 8 cells --> 8 bits.\n- The bit position determines the cell index, in Morton Order.\n- The bit value determines if the cell is occupied or not.\n- If a cell is occupied, an additional octree may be generated in the next level, up till level 3.\nFor example, an entry of 00000001 is a single level octree, where only the bottom-left most cell is occupied.\n"
        }
       ]
      }
     },
     "e5c7c6ce87174710bf937e7f62d96629": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "VBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_0a2eec38d87d4f22962c9fc928b9670b"
       ],
       "layout": "IPY_MODEL_f63c2e6ff4bd48afad58d69380b57a3e"
      }
     },
     "e74512a2c7c643b698c71b432b076be3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "e7fc92253f504c4b800574a69a7f93c5": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_7f7a570f2e20449fa382eb4694674817",
        "IPY_MODEL_6ca65f5188c14df8955d21613ba1195c"
       ],
       "layout": "IPY_MODEL_335a2a7e5ead4c6d88f004629ce1752f"
      }
     },
     "e9513adb65ca4595bfdf9d31cee4153a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "e953e8cee6cb4085816e626147a79efc": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "ed938282f5df4659a8bfae78f54ce43f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "ee61f0297eff4e5e9750156de8764cd7": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f0cdec2c173f4710809ed6bda21d98fe": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "border": "0.2px dashed black"
      }
     },
     "f2ae2f54d1694ea0baed809823bca1b6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_e5c7c6ce87174710bf937e7f62d96629",
        "IPY_MODEL_66a4e262c16c41d1b79e72914f4adbb1"
       ],
       "layout": "IPY_MODEL_fa9c383e40a84b288760fa34327a2644"
      }
     },
     "f3585de08e7b49df8d257fadfb5d2018": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f63c2e6ff4bd48afad58d69380b57a3e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f8dea819dfec48f097fe9e1d13284cf8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_b75d3608c36b489fb0311c996f7ab57e"
       ],
       "layout": "IPY_MODEL_85450606f352421e80a37a6c6c70a8d5"
      }
     },
     "fa9c383e40a84b288760fa34327a2644": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "fcf3e5b018cc497291c1b442be8d1057": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "fe2728dd78764cf19024949c83a80bfa": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "children": [
        "IPY_MODEL_b2f3a2e38a454f9f95645d2f34a101ee",
        "IPY_MODEL_7358d7bc25ed497e96f3c60e50777092"
       ],
       "layout": "IPY_MODEL_349677fe5b39486b9a83526058aec8cf"
      }
     },
     "fe521de6840b4e6e83f223978a491109": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_0696668f859746d68fa2fa7200423ab9",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\"octrees\" represents a hierarchy of 17 octree nodes.\nEach bit represents a cell occupancy:\n\nLevel \u001B[31m#1, \u001B[0mPath\u001B[31m*\u001B[0m,        11111111\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m,      10000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m,      01000000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m,      00100000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m,      00010000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m,      00001000\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m,      00000100\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m,      00000010\nLevel \u001B[34m#2, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m,      00000001\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m1\u001B[0m-\u001B[32m8\u001B[0m,    01111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m2\u001B[0m-\u001B[32m7\u001B[0m,    10111111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m3\u001B[0m-\u001B[32m6\u001B[0m,    11011111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m4\u001B[0m-\u001B[32m5\u001B[0m,    11101111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m5\u001B[0m-\u001B[32m4\u001B[0m,    11110111\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m6\u001B[0m-\u001B[32m3\u001B[0m,    11111011\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m7\u001B[0m-\u001B[32m2\u001B[0m,    11111101\nLevel \u001B[32m#3, \u001B[0mPath\u001B[31m*\u001B[0m-\u001B[34m8\u001B[0m-\u001B[32m1\u001B[0m,    11111110\n"
        }
       ]
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}